Simply put, a token is the smallest meaningful unit of text that artificial intelligence (AI) models process. It can be a single word, part of a word, or even a single character.
Tokenization is the technique AI systems use to break down sentences and paragraphs into tokens, allowing machine learning models like GPT to interpret text more effectively. Because language is often complex, tokenization streamlines how AI detects patterns, extracts meaning, and generates coherent responses.
Tokens in AI represent the building blocks of language for computational models. Think of them as puzzle pieces that fit together to form a coherent text. Whether these pieces are entire words, subwords, or characters, tokens help AI systems like GPT, BERT, and others understand linguistic constructs in a structured way.
For example, a model analyzing the sentence “I love Jotform!” might see it as three tokens (“I,” “love,” and “Jotform!”) in a word-based system or more tokens if it uses subword tokenization. Each token is then converted into numerical representations that the AI processes to generate predictions or responses.
Tokenization: How AI breaks down text
Tokenization is the process of splitting text into tokens so that AI can interpret language at a granular level. This process involves identifying boundaries — often spaces or punctuation marks — and separating text accordingly. For AI to understand and generate responses, it needs a systematic way to handle language, and that’s exactly what tokenization provides.
Once text is tokenized, AI models convert tokens into numerical vectors that represent semantic or syntactic relationships. These vectors then flow through neural network layers, enabling the model to detect patterns, predict upcoming words, or classify text by topic. Tokenization lays the groundwork for how AI reads and interprets written language.
Types of tokenization in AI
Different AI applications use various tokenization methods, each with pros and cons. Here’s a quick rundown:
Word tokenization
In word tokenization, text is split based on spaces and punctuation to isolate words. This method works well for languages that separate words with spaces, but it struggles with languages that use complex character systems. It can also be inefficient if the vocabulary is large, as each word becomes its own token.
Subword tokenization
Subword tokenization, popular in models like GPT and BERT, breaks words into smaller units. This approach helps address out-of-vocabulary issues and reduces the token count for frequent root words. Tools like WordPiece or SentencePiece use statistical methods to decide how to best split words based on their frequency in a training corpus.
Character tokenization
Character-level tokenization treats each character as a token. While this method guarantees coverage for any language or symbol, it often results in longer sequences, making training slower. However, it can benefit languages with rich morphological structures or tasks where subtle character differences significantly impact the text’s meaning.
Byte pair encoding (BPE)
Byte pair encoding (BPE) merges the most frequent pairs of characters or subwords iteratively. This method effectively compresses text into tokens while still being flexible enough to handle rare words. BPE is widely adopted in transformer architectures and balances vocabulary size and model performance, making it a top choice for many NLP tasks.
Why do AI models use tokens instead of words?
Using tokens rather than entire words offers multiple advantages. For one, it helps manage large vocabulary sizes. In English alone, there are around 171,000 words in current use (according to the Oxford English Dictionary), and that’s without considering technical jargon or other languages.
Tokenization also tackles the problem of out-of-vocabulary (OOV) words. Instead of discarding an unknown word entirely, subword tokenization can break it down into recognizable segments. This approach ensures the AI can still extract meaning from new or uncommon terms, making the model more robust and adaptable.
Tokens in AI language models (GPT, BERT, etc.)
Models like OpenAI’s GPT series rely heavily on subword tokenization. GPT converts input text into tokens, each mapped to an embedding vector. These vectors pass through multiple transformer layers, capturing context from surrounding tokens. The ability to handle words, subwords, and even punctuation as discrete tokens empowers GPT to generate highly coherent and context-aware text.
BERT, on the other hand, uses WordPiece, a subword tokenization technique that splits words into frequent units. Both GPT and BERT have token limits. For example, GPT-4 can handle over 8,000 tokens in one go, although higher limits can result in increased computational costs. If the token limit is exceeded, the model might truncate or ignore parts of the input.
The future of tokenization in AI
As AI language models continue to evolve, tokenization techniques will also advance. Researchers are developing adaptive tokenization systems that adjust splits dynamically based on context or domain. This could help models better understand idiomatic expressions, technical jargon, or even code snippets, leading to more accurate and humanlike responses.
In addition, some experts are exploring tokenization strategies for multimodal AI, which involves processing not just text, but also images, audio, and other data types. Advances in this area could enable unified models that excel at tasks spanning multiple modalities, from captioning images to answering questions about audio clips, all thanks to more nuanced token management.
Understanding tokens and how they function in AI-driven text processing can help organizations optimize their machine-learning pipelines. Whether you’re building a chatbot, classifying documents, or generating content, tokenization is at the core of modern NLP solutions, bridging the gap between raw text and meaningful AI-driven insights.
For further exploration, check out OpenAI’s Tokenizer Documentation or Hugging Face’s Transformers library to see how tokenization algorithms are implemented in real-world AI workflows. Mastering tokenization is a key step toward AI solutions.
Photo by Artem Podrez
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